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Model: AmareshHebbar/icd10-coder-qwen25-7b-merged Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/qwen2.5-7b-instruct
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- clinical-nlp
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- medical
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- insurance
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- qwen2.5
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- safetensors
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- gguf
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- icd10
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- healthcare
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- fine-tuned
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license: apache-2.0
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language:
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- en
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widget:
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- text: "Patient presents with acute appendicitis requiring surgical intervention. Admitted for 2 days. What ICD-10 codes apply and what insurance coverage would WHO guidelines indicate?"
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example_title: "Appendicitis + Coverage Query"
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- text: "Diagnosis: Type 2 diabetes mellitus with diabetic nephropathy. What ICD-10 coding and reimbursement brackets apply?"
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example_title: "Chronic Condition Coding"
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spaces:
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- AmareshHebbar/icd10-coder-demo
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---
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<div align="center">
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# ICD-10 Medical Coder — Qwen2.5-7B
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### An AI system for WHO-standardized medical classification, insurance code prediction, and coverage estimation
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[](https://huggingface.co/unsloth/qwen2.5-7b-instruct)
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[](LICENSE)
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[](https://api.wandb.ai/links/amareshhebbar-/qonl6s58)
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[](https://api.wandb.ai/links/amareshhebbar-/qonl6s58)
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[](https://github.com/amareshhebbar/AxisMapper)
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</div>
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---
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## What Is This?
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**ICD-10-Coder** is the first model in a long-term initiative — [**AxisMapper**](https://github.com/amareshhebbar/AxisMapper) — to build an AI-native insurance intelligence layer for the Indian and global healthcare ecosystem.
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The **International Classification of Diseases, 10th Revision (ICD-10)**, maintained by the **World Health Organization (WHO)**, is the globally accepted standard for encoding medical diagnoses, procedures, and conditions. Every hospital, insurer, and government health authority uses ICD-10 codes to classify care and determine reimbursement.
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The core insight behind this project: **insurance agents, hospital billing teams, and patients have no reliable way to know what a given diagnosis actually entitles them to**. Coverage decisions are opaque, rules are fragmented across schemes, and the same condition might be coded five different ways — each triggering a different payout.
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This model is the **first agent** in what will become a **Multi-Agent, Mixture-of-Experts (MoE) pipeline** — purpose-built to decode that opacity.
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---
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## The Bigger Vision: AxisMapper
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> *"One fine-tuned model per insurance scheme. A shared routing layer. Zero ambiguity for the patient."*
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India's health insurance landscape spans:
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- **Ayushman Bharat / PM-JAY** — world's largest government-funded health insurance scheme
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- **Star Health** — India's largest standalone health insurer
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- **ESIC / CGHS** — central government employee schemes
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- **State-level programs** — varying eligibility, tariff, and admission rules
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- **NGO-backed schemes** — community-level coverage with entirely different logic
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Each of these schemes has its own ICD-10 code mappings, admission duration requirements, procedure eligibility, and claim caps. There is no unified interface to query them all.
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**AxisMapper's roadmap:**
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```
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Phase 1 (Now) → WHO ICD-10 base model (this model)
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Universal code prediction + coverage logic
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Phase 2 → Fine-tune per scheme (StarHealth, PM-JAY, ESIC, etc.)
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Each model specialises in one insurer's rule set
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Phase 3 → MoE Router
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Given a patient + insurer, route to the right specialist model
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Phase 4 → Multi-Agent Pipeline
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Agent 1: Diagnosis → ICD-10 code
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Agent 2: Code → Coverage estimate (policy-aware)
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Agent 3: Coverage + Admission rules → Final claim amount
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Agent 4: Web search → Real-time tariff / market validation
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```
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This model — the WHO-standardized base — handles **Phase 1**: given any clinical description, it returns the correct ICD-10 code, explains the classification, and applies WHO-level coverage logic.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base Model** | `unsloth/qwen2.5-7b-instruct` |
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| **Architecture** | Qwen2 (decoder-only transformer) |
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| **Parameters** | ~8B |
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| **Precision** | BF16 |
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| **Fine-tuning Method** | LoRA via Unsloth + HuggingFace TRL |
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| **Training Hardware** | NVIDIA RTX A5000 (24GB VRAM) |
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| **Training Duration** | ~2 hours |
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| **Training Speed** | 2× faster than standard HF training (via Unsloth) |
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| **Experiment Tracking** | Weights & Biases (W&B) |
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| **Max Sequence Length** | 2048 tokens |
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| **License** | Apache 2.0 |
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---
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## Training Infrastructure
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This model was trained using the [Unsloth](https://github.com/unslothai/unsloth) optimization library, which achieves **2× training speed** and **~60% VRAM reduction** compared to standard HuggingFace fine-tuning — without any loss in model quality.
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**Training stack:**
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- `unsloth` — optimized LoRA fine-tuning engine
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- `trl` (HuggingFace) — SFTTrainer for instruction fine-tuning
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- `transformers` — model loading, tokenization, inference
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- `wandb` — real-time loss curves, learning rate scheduling, gradient tracking
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All training runs are logged and reproducible via Weights & Biases. The training converged stably within 2 hours on a single A5000 GPU, making this a cost-efficient approach to medical domain adaptation.
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---
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## What This Model Does
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Given a clinical description or patient scenario, this model will:
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1. **Assign the correct ICD-10 code(s)** — primary diagnosis, secondary conditions, procedure codes
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2. **Explain the WHO classification logic** — why this code, what the category means, adjacent codes
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3. **Estimate WHO-level insurance coverage** — standard reimbursement brackets, admission duration requirements, procedure eligibility
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4. **Flag restrictions** — minimum admission days, co-morbidity requirements, pre-authorisation triggers
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5. **Support multi-condition scenarios** — comorbidities, complications, dual coding
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**Example input:**
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```
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Patient admitted for acute appendicitis with peritonitis.
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Underwent emergency appendectomy. Admitted for 3 days.
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What ICD-10 codes apply and what is the expected insurance coverage?
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```
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**Example output (truncated):**
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```
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Primary Code: K35.2 — Acute appendicitis with generalised peritonitis
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Procedure Code: 0DTJ4ZZ — Resection of appendix, percutaneous endoscopic approach
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WHO Classification: Diseases of the digestive system (K00–K93)
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Chapter XI, Block K35-K38 (Diseases of appendix)
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Coverage Logic:
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- WHO standard: Surgical admission, inpatient required
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- Minimum admission: 1–3 days (surgery-dependent)
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- Reimbursement class: Major surgery
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- Pre-auth: Required for elective; emergency bypass available
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- Approximate WHO-tier bracket: ₹35,000–₹75,000 (India tier-2 hospital)
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```
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---
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## Quickstart
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### Using Transformers (Pipeline)
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="AmareshHebbar/icd10-coder-qwen25-7b-merged")
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query = """
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Patient presents with Type 2 diabetes mellitus with chronic kidney disease stage 3.
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What ICD-10 codes apply? What are the WHO-level insurance implications?
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What are the admission requirements for this to be covered?
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"""
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result = pipe([{"role": "user", "content": query}], max_new_tokens=512)
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print(result[0]["generated_text"][-1]["content"])
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```
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### Using Unsloth (Recommended for inference speed)
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```python
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from unsloth import FastModel
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model, tokenizer = FastModel.from_pretrained(
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model_name="AmareshHebbar/icd10-coder-qwen25-7b-merged",
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max_seq_length=2048,
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load_in_4bit=True, # Optional: 4-bit for lower VRAM
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)
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messages = [
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{"role": "system", "content": "You are an expert ICD-10 medical coder with deep knowledge of WHO insurance classification standards."},
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{"role": "user", "content": "Patient: acute MI, stented. 2-day admission. Code and coverage?"}
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]
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inputs = tokenizer.apply_chat_template(
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messages, tokenize=True, add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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### Using vLLM (Production / High Throughput)
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```bash
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pip install vllm
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vllm serve "AmareshHebbar/icd10-coder-qwen25-7b-merged" --max-model-len 2048
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```
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
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response = client.chat.completions.create(
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model="AmareshHebbar/icd10-coder-qwen25-7b-merged",
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messages=[
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{"role": "system", "content": "You are an expert ICD-10 coder and insurance analyst."},
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{"role": "user", "content": "Patient: fractured femur, open reduction required, 4-day inpatient. ICD-10 codes and insurance coverage?"}
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],
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max_tokens=512,
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temperature=0.1,
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)
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print(response.choices[0].message.content)
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```
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### Using Ollama (Local / Offline)
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```bash
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# Export to GGUF first (via llama.cpp or Unsloth export)
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ollama create icd10-coder -f ./Modelfile
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ollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?"
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```
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---
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## 🔌 Integrations Supported
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| Backend | Status | Use Case |
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|---|---|---|
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| HuggingFace Transformers | ✅ | Research, prototyping |
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| Unsloth FastModel | ✅ | Fast inference, fine-tuning |
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| vLLM | ✅ | Production API, high throughput |
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| SGLang | ✅ | Structured generation |
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| Ollama | ✅ | Local / offline deployment |
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| Claude API (Anthropic) | 🔌 Planned | Hybrid: ICD-10 code → Claude for coverage analysis |
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| Gemini API (Google) | 🔌 Planned | Multi-LLM comparison layer |
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| Web Search (Tavily/Serper) | 🔌 Planned | Real-time tariff + hospital rate lookup |
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---
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## ICD-10 Coverage
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This model has been fine-tuned across all major ICD-10-CM chapters:
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| Chapter | Description |
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| I (A00–B99) | Infectious and parasitic diseases |
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| II (C00–D49) | Neoplasms |
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| III (D50–D89) | Blood and immune disorders |
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| IV (E00–E89) | Endocrine, nutritional, metabolic |
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| V (F01–F99) | Mental and behavioural disorders |
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| IX (I00–I99) | Circulatory system diseases |
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| X (J00–J99) | Respiratory diseases |
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| XI (K00–K95) | Digestive system diseases |
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| XIII (M00–M99) | Musculoskeletal diseases |
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| XIV (N00–N99) | Genitourinary diseases |
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| XIX (S00–T88) | Injuries, poisonings |
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| XXI (Z00–Z99) | Health status, contact with services |
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---
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## Limitations & Intended Use
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- This model is trained on **WHO ICD-10 baseline standards**, not on any specific insurer's proprietary rules. Coverage estimates are **indicative**, not legally binding.
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- **Not a substitute for professional medical coding** or licensed insurance adjudication.
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- Coverage estimates should be validated against the patient's actual policy terms and the treating hospital's empanelment status.
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- Future scheme-specific models (Ayushman Bharat, Star Health, etc.) will provide more precise, policy-aware outputs.
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---
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## Links
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- **GitHub (AxisMapper):** [https://github.com/amareshhebbar/AxisMapper](https://github.com/amareshhebbar/AxisMapper)
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- **Developed by:** [AmareshHebbar](https://huggingface.co/AmareshHebbar)
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- **Base model:** [unsloth/qwen2.5-7b-instruct](https://huggingface.co/unsloth/qwen2.5-7b-instruct)
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---
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## Citation
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```bibtex
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@misc{hebbar2025icd10coder,
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title={ICD-10 Coder: A Fine-tuned Qwen2.5-7B for Medical Classification and Insurance Coverage Estimation},
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author={Amaresh Hebbar},
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year={2025},
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publisher={HuggingFace},
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url={https://huggingface.co/AmareshHebbar/icd10-coder-qwen25-7b-merged},
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note={Part of the AxisMapper project: https://github.com/amareshhebbar/AxisMapper}
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}
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```
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---
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<div align="center">
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<sub>Built with Unsloth · Trained on A5000 · Tracked with W&B · Part of AxisMapper</sub>
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</div>
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